Are you a seasoned engineer ready to solve complex, high-impact problems at one of the world's most influential financial institutions? At JPMorganChase, we invest in our engineers and give them the tools, autonomy, and scale to build technology that matters — for millions of customers and the global economy.
As a Senior Principal Software Engineer at JPMorganChase within Corporate Technology, you will serve as a technical authority driving the design and delivery of large-scale, cloud-native systems that power critical business capabilities across the firm and specifically within Corporate Treasury. Corporate Treasury operates across a broad remit with responsibilities that include managing the firm’s structural interest rate risk and foreign exchange (FX) hedging strategy; executing debt issuance to support the firm’s funding needs; overseeing the Chief Investment Office (CIO) / investment portfolio; and developing and calculating Fund Transfer Pricing (FTP) to allocate internal funding and liquidity costs, benefits, and incentives across lines of business. You will influence engineering strategy, set technical direction, and partner with senior leaders and cross-functional teams to solve some of the most complex challenges in financial technology. Your work will directly shape how the firm builds, scales, and modernizes its technology landscape.
Job Responsibilities
- Architect and deliver highly scalable, cloud-native solutions on AWS, applying modern microservices and API-first design principles across enterprise platforms
- Define and champion engineering standards, patterns, and best practices for software development, DevOps tooling, and continuous delivery pipelines
- Lead technical design reviews and provide authoritative guidance on system architecture, ensuring solutions meet performance, reliability, and security requirements
- Drive the adoption of modern DevOps practices, including infrastructure as code, CI/CD automation, and observability frameworks across engineering teams
- Develop and maintain robust, production-grade services using Python or equivalent modern programming languages, with a focus on code quality and maintainability
- Collaborate with product, architecture, and business stakeholders to translate complex requirements into scalable technical solutions with measurable outcomes
- Identify and resolve systemic technical risks, performance bottlenecks, and architectural gaps across distributed systems
- Mentor and influence senior engineers across teams, elevating technical craft and fostering a culture of engineering excellence
- Design, build, and deploy AI-enabled solutions to meet business needs
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience
- Deep expertise in AWS cloud services, including compute, networking, storage, and managed services, with hands-on experience designing and operating production workloads
- Proven experience building and scaling microservices architectures and RESTful or event-driven APIs in distributed, high-availability environments
- Strong proficiency in Python or comparable modern programming languages, with a demonstrated ability to write clean, testable, and maintainable code
- Proven experience designing, building, and deploying AI-enabled solutions to meet business needs.
- Extensive experience with modern DevOps practices and tooling, including CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and container orchestration (e.g., Kubernetes, Docker)
- Demonstrated ability to lead technical decision-making and influence engineering direction across large, cross-functional teams
- Strong analytical and problem-solving skills with experience diagnosing and resolving complex issues in distributed systems at scale
- Excellent communication skills with the ability to present technical concepts clearly to both technical and non-technical audiences
Preferred qualifications, capabilities, and skills
- Experience applying artificial intelligence or machine learning techniques within enterprise software platforms or data-intensive applications
- Hands-on experience with big data technologies such as Apache Spark, Kafka, Hadoop, or cloud-native data platforms (e.g., AWS EMR, Glue, or Redshift)
- Exposure to platform engineering or internal developer platform (IDP) initiatives at enterprise scale